{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b6050099-4cfb-4e3f-8c52-80461d5cab91",
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "288a3c92-abcb-45cd-830a-3fc7a66fe273",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run -i \"../util/lang_utils.ipynb\"\n",
    "%run -i \"../util/file_utils.ipynb\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7e8c4dca-398d-4eec-93d6-4b1562d8e158",
   "metadata": {},
   "outputs": [],
   "source": [
    "text_file = \"../data/sherlock_holmes.txt\"\n",
    "text = read_text_file(text_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "15fbf91c-7b03-41ec-bc6e-3bd96919fe80",
   "metadata": {},
   "outputs": [],
   "source": [
    "past_tags = [\"VBD\", \"VBN\"]\n",
    "present_tags = [\"VBG\", \"VBP\", \"VBZ\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2171e4ba-6207-4333-b93b-e14ce33babb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_verbs(text, nlp):\n",
    "    doc = nlp(text)\n",
    "    verb_dict = {\"Inf\":0, \"Past\":0, \"Present\":0}\n",
    "    for token in doc:\n",
    "        if (token.tag_ == \"VB\"):\n",
    "            verb_dict[\"Inf\"] = verb_dict[\"Inf\"] + 1\n",
    "        if (token.tag_ in past_tags):\n",
    "            verb_dict[\"Past\"] = verb_dict[\"Past\"] + 1\n",
    "        if (token.tag_ in present_tags):\n",
    "            verb_dict[\"Present\"] = verb_dict[\"Present\"] + 1\n",
    "    plt.bar(range(len(verb_dict)), list(verb_dict.values()), align='center', color=[\"red\", \"green\", \"blue\"])\n",
    "    plt.xticks(range(len(verb_dict)), list(verb_dict.keys()))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ddfc2132-11a4-4b23-b2bf-7307e73ea750",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_verbs(text, small_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b846629-8c6d-4cfb-92dd-d688be6d536e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
